Prediction of water quality which can ensure the water supply and prevent water pollution is essential for a successful water transfer project. In recent years, with the development of artificial intelligence, the backpropagation (BP) neural network has been increasingly applied for the prediction and forecasting field. However, the BP neural network frame cannot satisfy the demand of higher accuracy. In this study, we extracted monitoring data from the water transfer channel of both the water resource and the intake area as training samples and selected some distinct indices as input factors to establish a BP neural network whose connection weight values between network layers and the threshold of each layer had already been optimized by an improved artificial bee colony (IABC) algorithm. Compared with the traditional BP and ABC-BP neural network model, it was shown that the IABC-BP neural network has a greater ability for forecasting and could achieve much better accuracy, nearly 25% more precise than the BP neural network. The new model is particularly practical for the water quality prediction of a water diversion project and could be readily applied in this field.
The water characteristics of the Gucheng Lake, such as eutrophication, health and spatial distribution, were investigated. On the basis of the trophic state index (TSI) and entropy weight, a synthesized trophic state index (STSI) model was established to assess lake eutrophication condition through calculating STSI, choosing TP, TN, COD, BOD and NH 3 -N as trophic variables. The STSI ranged from 50.58 to 62.44, which showed that the water has been between eutrophic and supereutrophic. A histogram was applied to health risk assessment which was analyzed from carcinogenic substances (Cr ?6 , As and Cd) and non-carcinogenic substances (hydroxybenzene, Pb, Hg, CN -and NH 3 ), and the results showed that the former was much greater than the latter for effect. The total risk for each resident caused by all pollutants ranged from 5.18E-05 to 8.34E-05, which is far higher than the standard, recommended by Sweden Bureau of Environment Protection and Holland Ministry of Building and Environment Protection (1.0E-05). Cluster analysis was used to detect similarities and dissimilarities among the seven sampling sites and explain the observed clustering in terms of affected conditions. Twenty-one variables were used to divide seven sampling sites into three groups, namely, north lake, south lake and lake center.
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